Computational insights into the structure and dynamics of the human serotonin transporter N-terminus by microsecond molecular dynamics

2020 ◽  
Vol 17 ◽  
Author(s):  
Sorin Draga ◽  
Laura Olariu ◽  
Speranta Avram

Background: The human serotonin transporter is an important drug target for the treatment of various medical conditions of which depression is the most important, but also include attention deficit hyperactivity disorder, schizophrenia, social anxiety disorder and irritable bowel syndrome, among others. The transmembrane portion of the human transporter has been studied extensively and first crystalized in 2016. However, the dynamical nature of the N-terminal segment of pro-tein and its post-translational modifications remain insufficiently explored. Objective: The present study aims to evaluate the structure and dynamics of the N-terminal segment of the human serotonin transporter and the presence and stability of possible secondary structure elements along with its post-translational modifica-tions and disorder propensity. Methods: The segment was investigated using a combination of bioinformatics tools for physico-chemical characterization, secondary structure prediction, post-translational modifications and disorder prediction, followed by ab initio modeling and microsecond long explicit solvent molecular dynamics. Results: Our study reveals the presence of metastable secondary structure elements, namely two alpha helices and a beta-sheet, throughout the molecular dynamics run and identifies numerous sites with high probability for post-translational mod-ifications. Conclusion: Our results show that, despite the intrinsically unstructured nature, the N-terminus adopts a stable confor-mation with stable secondary structure elements, that could indicate an important functional role for the segment. Also, there is a high probability that the segment undergoes multiple post-translational modifications.

2019 ◽  
Vol 16 (2) ◽  
pp. 159-172 ◽  
Author(s):  
Elaheh Kashani-Amin ◽  
Ozra Tabatabaei-Malazy ◽  
Amirhossein Sakhteman ◽  
Bagher Larijani ◽  
Azadeh Ebrahim-Habibi

Background: Prediction of proteins’ secondary structure is one of the major steps in the generation of homology models. These models provide structural information which is used to design suitable ligands for potential medicinal targets. However, selecting a proper tool between multiple Secondary Structure Prediction (SSP) options is challenging. The current study is an insight into currently favored methods and tools, within various contexts. Objective: A systematic review was performed for a comprehensive access to recent (2013-2016) studies which used or recommended protein SSP tools. Methods: Three databases, Web of Science, PubMed and Scopus were systematically searched and 99 out of the 209 studies were finally found eligible to extract data. Results: Four categories of applications for 59 retrieved SSP tools were: (I) prediction of structural features of a given sequence, (II) evaluation of a method, (III) providing input for a new SSP method and (IV) integrating an SSP tool as a component for a program. PSIPRED was found to be the most popular tool in all four categories. JPred and tools utilizing PHD (Profile network from HeiDelberg) method occupied second and third places of popularity in categories I and II. JPred was only found in the two first categories, while PHD was present in three fields. Conclusion: This study provides a comprehensive insight into the recent usage of SSP tools which could be helpful for selecting a proper tool.


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